DESlib: A Dynamic Ensemble Selection Library in Python

Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti

JMLR 2020 pp. 1-5

/jmlr/2020/cruz2020jmlr-deslib/

Abstract

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.

PDF JMLR Code Semantic Scholar

Cite

Text

Cruz et al. "DESlib: A Dynamic Ensemble Selection Library in Python." Journal of Machine Learning Research, 2020.

Markdown

[Cruz et al. "DESlib: A Dynamic Ensemble Selection Library in Python." Journal of Machine Learning Research, 2020.](https://mlanthology.org/jmlr/2020/cruz2020jmlr-deslib/)

BibTeX

@article{cruz2020jmlr-deslib,
  title     = {{DESlib: A Dynamic Ensemble Selection Library in Python}},
  author    = {Cruz, Rafael M. O. and Hafemann, Luiz G. and Sabourin, Robert and Cavalcanti, George D. C.},
  journal   = {Journal of Machine Learning Research},
  year      = {2020},
  pages     = {1-5},
  volume    = {21},
  url       = {https://mlanthology.org/jmlr/2020/cruz2020jmlr-deslib/}
}